The invention relates to methods and systems that provide computerized health care management, and more specifically, to methods and systems providing statistical assessment and prognostic information for individuals.
This application hereby incorporates by reference in its entirety another application filed on even date by the same inventors as and entitled “Methods and Apparatus for Providing Decision Support.”
The past decade of health services research has witnessed an explosion of prognostic models to help physicians understand the risks and benefits of proposed medical therapies. However, the application of such models to clinical practice has been limited by both their complexity and the lack of a practical mechanism for making them available at the time of medical decision-making.
Furthermore, both the patient and the attending health care professional should be involved in making clinical health care treatment decisions that affect the patient's desired heath goals and quality of life concerns. These decisions may vary depending upon the patient's age, sex, socioeconomic, demographic and clinical characteristics, genetic, proteomic and biomarker parameters. The factors affect the relative risk and successfulness of outcomes of medical and surgical procedures and are referred to in the art by the term “clinical data.”
One goal of medical care is to make treatment recommendations to patients commensurate with their goals and values. To achieve this goal, one must describe the risks and benefits of a treatment that are relevant to a unique constellation of comorbidities and function of each particular patient. Several known risk-stratification models exist for diseases ranging from coronary artery disease to deep venous thrombosis to mortality from a gastrointestinal hemorrhage, e.g., as reported in Knaus W A et al., APACHEII: a severity of disease classification system. Critical Care Medicine 13(10) 818-29 (1985, October) (a model for ICU patients); Wells PS et al., Accuracy of clinical assessment of deep-vein thrombosis. Lancet. 345:1326-30 (1995) (a score for the probability of deep vein thrombosis); Rockall TA et al., Risk assessment after acute upper gastrointestinal haemorrhage. Gut 38:316-21 (1996). Currently, these models remain largely academic and without a practical method or mechanism for being used in routine clinical care.
Computerized expert systems process information that usually correspond to rules or procedures that are applied by human experts to solve similar problems. One example of an expert system in the health care field is described in U.S. Pat. No. 5,517,405 to McAndrew et al. This system prompts users with questions directed towards a particular medical condition or symptom. The system consults a database or library to dynamically generate queries on the basis of user responses. Ultimately, the system provides a diagnosis together with a recommendation for a medical procedure or other treatment.
Another example of an expert system in the health care field is described in U.S. Pat. No. 6,385,589 to Trusheim et al. The system described therein accepts input from data sources that target identification of a “medical event.” The data sources include laboratory data confirming a disease diagnosis, pharmacy benefits manager data, hospital admissions data, physician records, home health care data, and health insurance data. The system described by Trusheim et al. also provides member characterization data, which is compiled using member surveys, care provider data, insurance claim data, and historical health care data. The system uses a combination of member characteristics and medical event data to identify risk situations, such as diabetes, hypertension, dementia, unmedicated heart conditions, and discharge of elderly patients with no caregiver support. Although member survey responses may be used in making these risk assessments, Trusheim et al. does not appear to use the survey responses to identify the nature and extent of risk in terms of a statistical risk assessment. For example, a condition may be diagnosed in which an elderly person has been discharged with no caregiver, but without any assessment of whether the discharge poses mild or severe health status risk. Proactive follow-ups after identification of a risk situation are left to a medical care provider who assesses the patient.
Other known methods for evaluating and risk-stratifying patients to (for example) identify those with the greatest likelihood of future coronary conditions include exercise treadmill tests, nuclear stress tests, and stress echocardiography. Once these tests identify patients with an adverse prognosis, further risk-stratifying patients are expensive and, in some cases, invasive. Additionally, the results from such tests can require several days for a trained technician or statistician to process and there is no clear mechanism for integrating the data in statistical models to better refine risk or to determine how such risk may vary as a function of alternative treatment strategies. Most importantly, the subsequent outcomes of alternative treatment strategies for a given risk profile are neither generated nor presented.
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